Introduction to the theory of neural computation
Introduction to the theory of neural computation
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Distributed Representations, Simple Recurrent Networks, And Grammatical Structure
Machine Learning - Connectionist approaches to language learning
The Induction of Dynamical Recognizers
Machine Learning - Connectionist approaches to language learning
Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Analog computation via neural networks
Theoretical Computer Science
On the computational power of neural nets
Journal of Computer and System Sciences
Neural Computation
Natural Language Grammatical Inference with Recurrent Neural Networks
IEEE Transactions on Knowledge and Data Engineering
Rule Extraction from Recurrent Neural Networks: A Taxonomy and Review
Neural Computation
Introduction to Automata Theory, Languages, and Computation (3rd Edition)
Introduction to Automata Theory, Languages, and Computation (3rd Edition)
Finite state automata and simple recurrent networks
Neural Computation
LSTM recurrent networks learn simple context-free and context-sensitive languages
IEEE Transactions on Neural Networks
A cognitive interactionist sentence parser with simple recurrent networks
Information Sciences: an International Journal
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The performance of a simple recurrent neural network on the implicit acquisition of a context-free grammar is re-examined and found to be significantly higher than previously reported by Elman. This result is obtained although the previous work employed a multilayer extension of the basic form of simple recurrent network and restricted the complexity of training and test corpora. The high performance is traced to a well-organized internal representation of the grammatical elements, as probed by a principal-component analysis of the hidden-layer activities. From the next-symbol-prediction performance on sentences not present in the training corpus, a capacity of generalization is demonstrated.